skip to main content
10.1145/3520304.3528763acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
poster

Minimal criterion artist collective

Published:19 July 2022Publication History

ABSTRACT

Minimal criterion co-evolution (MCC) is an evolutionary algorithm that uses a simple reproduction constraint between two interacting populations to drive an open-ended search process. While it has previously been applied to parameterise simple agents and environments, in this work we extend its use to the generation of art: synthesising both images and music. As a creative AI tool which does not require any data, the use of MCC emphasises the design of the creative medium.

Skip Supplemental Material Section

Supplemental Material

References

  1. Margaret A Boden and Ernest A Edmonds. 2009. What is Generative Art? Digit. Creat. 20, 1--2 (2009), 21--46.Google ScholarGoogle ScholarCross RefCross Ref
  2. Sam Bond-Taylor, Adam Leach, Yang Long, and Chris G Willcocks. 2021. Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models. arXiv:2103.04922 (2021).Google ScholarGoogle Scholar
  3. Jonathan C Brant and Kenneth O Stanley. 2017. Minimal Criterion Coevolution: A New Approach to Open-ended Search. In GECCO.Google ScholarGoogle Scholar
  4. Jonathan C Brant and Kenneth O Stanley. 2020. Diversity Preservation in Minimal Criterion Coevolution Through Resource Limitation. In GECCO.Google ScholarGoogle Scholar
  5. Scott Draves and Erik Reckase. 2008. The Fractal Flame Algorithm. Technical Report. Spotworks.Google ScholarGoogle Scholar
  6. Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, SM Ali Eslami, and Oriol Vinyals. 2018. Synthesizing Programs for Images Using Reinforced Adversarial Learning. In ICML.Google ScholarGoogle Scholar
  7. Leon A Gatys, Alexander S Ecker, and Matthias Bethge. 2016. Image Style Transfer Using Convolutional Neural Networks. In CVPR.Google ScholarGoogle Scholar
  8. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In NeurIPS.Google ScholarGoogle Scholar
  9. Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. GANs Trained by a Two Time-scale Update Rule Converge to a Local Nash Equilibrium. In NeurIPS.Google ScholarGoogle Scholar
  10. Kiyohito Iigaya, Sanghyun Yi, Iman A Wahle, Koranis Tanwisuth, and John P O'Doherty. 2021. Aesthetic Preference for Art Can Be Predicted From a Mixture of Low- and High-level Visual Features. Nat. Hum. Behav. 5, 6 (2021), 743--755.Google ScholarGoogle ScholarCross RefCross Ref
  11. Tero Karras, Samuli Laine, and Timo Aila. 2019. A Style-based Generator Architecture for Generative Adversarial Networks. In CVPR.Google ScholarGoogle Scholar
  12. Kevin Kilgour, Mauricio Zuluaga, Dominik Roblek, and Matthew Sharifi. 2018. Fréchet Audio Distance: A Metric for Evaluating Music Enhancement Algorithms. arXiv:1812.08466 (2018).Google ScholarGoogle Scholar
  13. Joel Lehman and Kenneth O Stanley. 2008. Exploiting Open-endedness to Solve Problems Through the Search for Novelty. In ALIFE.Google ScholarGoogle Scholar
  14. Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, and Jason Yosinski. 2018. An Intriguing Failing of Convolutional Neural nNtworks and the CoordConv Solution. In NeurIPS.Google ScholarGoogle Scholar
  15. Claudio Mattiussi and Dario Floreano. 2003. Viability Evolution: Elimination and Extinction in Evolutionary Computation. Technical Report. Ecole Polytechnique Fédérale de Lausanne.Google ScholarGoogle Scholar
  16. Alexander Mordvintsev, Christopher Olah, and Mike Tyka. 2015. Inceptionism: Going Deeper into Neural Networks. https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.htmlGoogle ScholarGoogle Scholar
  17. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, et al. 2019. Pytorch: An Imperative Style, High-performance Deep Learning Library. In NeurIPS.Google ScholarGoogle Scholar
  18. Elena Popovici, Anthony Bucci, R Paul Wiegand, and Edwin D De Jong. 2012. Coevolutionary Principles. Springer, 987--1033.Google ScholarGoogle Scholar
  19. Justin K Pugh, Lisa B Soros, Paul A Szerlip, and Kenneth O Stanley. 2015. Confronting the Challenge of Quality Diversity. In GECCO.Google ScholarGoogle Scholar
  20. Alec Radford, Luke Metz, and Soumith Chintala. 2016. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. In ICLR.Google ScholarGoogle Scholar
  21. Juan J Romero and Penousal Machado. 2008. The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music. Springer Science & Business Media.Google ScholarGoogle Scholar
  22. Jimmy Secretan, Nicholas Beato, David B D Ambrosio, Adelein Rodriguez, Adam Campbell, and Kenneth O Stanley. 2008. Picbreeder: Evolving Pictures Collaboratively Online. In SIGCHI.Google ScholarGoogle Scholar
  23. L Soros and Kenneth O Stanley. 2014. Identifying Necessary Conditions for Open-ended Evolution Through the Artificial Life World of Chromaria. In ALIFE.Google ScholarGoogle Scholar
  24. Kenneth O Stanley. 2007. Compositional Pattern Producing Networks: A Novel Abstraction of Development. Genet. Program. Evolvable Mach. 8, 2 (2007), 131--162.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2018. Deep Image Prior. In CVPR.Google ScholarGoogle Scholar
  26. Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. 2019. Self-attention Generative Adversarial Networks. In ICML.Google ScholarGoogle Scholar

Index Terms

  1. Minimal criterion artist collective

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion
          July 2022
          2395 pages
          ISBN:9781450392686
          DOI:10.1145/3520304

          Copyright © 2022 Owner/Author

          Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 19 July 2022

          Check for updates

          Qualifiers

          • poster

          Acceptance Rates

          Overall Acceptance Rate1,669of4,410submissions,38%

          Upcoming Conference

          GECCO '24
          Genetic and Evolutionary Computation Conference
          July 14 - 18, 2024
          Melbourne , VIC , Australia
        • Article Metrics

          • Downloads (Last 12 months)19
          • Downloads (Last 6 weeks)2

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader